Multiscale dynamics and nonlinear associations of ecological quality in the Hangzhou Bay urban agglomeration, China

Ecological quality change in rapidly urbanizing regions is spatially heterogeneous, but assessments based on a single analytical scale may obscure local changes and alter the apparent factor importance. This study constructed a 2000–2024 Remote Sensing Ecological Index (RSEI) series for the Hangzhou Bay Urban Agglomeration using Google Earth Engine and Landsat imagery. Ecological changes were compared across 1, 3, 5, and 10 km grids and county units with XGBoost–SHAP model fitted to pooled observations from six benchmark years. Mean RSEI showed an early decline followed by sustained recovery, yielding a net increase of 0.0423. High values were concentrated in southwestern forested areas, whereas low values occurred mainly in the northern plain, densely urbanized bay areas, and urban-expansion corridors. The proportion of improved area increased from 75.34% at 1 km to 84.25% at the county scale. During 2015–2020, county-level aggregation increased the stable proportion while reducing the improved proportion, indicating that coarse units can mask opposing local changes. Land-use intensity had the largest relative mean absolute SHAP contribution at all scales (44.59%–55.43%), while the contribution of nighttime light intensity rose with aggregation and reached 20.33% at the county scale. These results demonstrate that ecological-change detection and interpretation of modeled associations are scale dependent, supporting a nested multiscale assessment framework.